Similar to existing works [1]}, [2]}, [3]}, we adopt the standard \(l_2\)  loss, perceptual loss [4]}, and adversarial loss for training. More details on the loss function can be found in the appendix.
To exploit the generative prior, we keep the weights of the latent bank fixed throughout training.
In our preliminary experiments, finetuning the latent bank with the encoder and decoder demonstrates no noticeable improvements. Moreover, it potentially harms the generalizability of the model as the latent bank may eventually bias to the training distribution.
It is worth emphasizing that despite GLEAN is trained with similar objectives as in existing works ( ESRGAN), the main difference to these methods is that GLEAN leverages a pre-trained generator to directly incorporate the priors into the network, further improving the output quality. We show that the improvement is not due to additional parameters in the generator by comparing GLEAN with ESRGAN\(^+\) , a larger ESRGAN that has similar FLOPs to GLEAN.
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